Prasad et al., 2023 - Google Patents
A Hybrid Time Series Rainfall Prediction Model Using Neural Prophet and LS TMPrasad et al., 2023
- Document ID
- 7699967322622680342
- Author
- Prasad G
- Teja B
- Haribabu T
- Pavani G
- Karunamma D
- Vivek K
- Publication year
- Publication venue
- 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
External Links
Snippet
India has a reputation for having frequent torrential downpours. Andhra Pradesh (AP) is inextricably linked to the catastrophe when the downpour is very heavy since it is the crucial area in Telugu-speaking states where the center of the government and non-government …
- 230000001537 neural effect 0 title abstract description 19
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Guo et al. | Short-term water demand forecast based on deep learning method | |
Zhang et al. | A hybrid deep learning-based method for short-term building energy load prediction combined with an interpretation process | |
Tran et al. | Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings | |
Torabi et al. | A hybrid machine learning approach for daily prediction of solar radiation | |
Zhao et al. | An optimized grey model for annual power load forecasting | |
Loy-Benitez et al. | Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders | |
Kisi et al. | Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models | |
Aqil et al. | Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool | |
Lv et al. | A long Short-Term memory cyclic model with mutual information for hydrology forecasting: A Case study in the xixian basin | |
Xiong et al. | Improved binary gaining–sharing knowledge-based algorithm with mutation for fault section location in distribution networks | |
Jiao | Application and prospect of artificial intelligence in smart grid | |
Fan et al. | Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China | |
Sogabe et al. | Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques | |
Prasad et al. | A Hybrid Time Series Rainfall Prediction Model Using Neural Prophet and LS TM | |
Muneer et al. | Short term residential load forecasting using long short-term memory recurrent neural network. | |
Luo et al. | Fuzzy cognitive map-enabled approach for investigating the relationship between influencing factors and prefabricated building cost considering dynamic interactions | |
Mohammad et al. | Short term load forecasting using deep neural networks | |
Baccarini | The maturing concept of estimating project cost contingency: A review | |
CN108205713A (en) | A kind of region wind power prediction error distribution determination method and device | |
Hemanth et al. | Proposing suitable data imputation methods by adopting a Stage wise approach for various classes of smart meters missing data–Practical approach | |
Li et al. | Mechanism of single variable grey forecasting modelling: integration of increment and growth rate | |
Jing et al. | A prediction model for building energy consumption in a shopping mall based on Chaos theory | |
Ahmad et al. | Efficient energy planning with decomposition-based evolutionary neural networks | |
Parviz et al. | Development of precipitation forecast model based on artificial intelligence and subseasonal clustering | |
Aquil et al. | Comparison of Machine Learning Models in Forecasting Reservoir Water Level |